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Found 13 papers, 5 papers with code
RS-Mamba for Large Remote Sensing Image Dense Prediction
RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images.
Ranked #1 on
Road Segmentation
on Massachusetts Roads Dataset
(F1 metric)
Building change detection for remote sensing images
Change Detection
+1
SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques
Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals.
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images
To this end, we propose a benchmark dataset for fine-grained Ship Instance Segmentation in Panchromatic satellite images, namely SISP, which contains 56, 693 well-annotated ship instances with four fine-grained categories across 10, 000 sliced images, and all the images are collected from SuperView-1 satellite with the resolution of 0. 5m.
LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model
Additionally, we introduce LHRS-Bench, a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding.
Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization
We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS).
Ranked #2 on
Change Detection
on WHU Building Dataset
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex.
ExARN: self-attending RNN for target speaker extraction
Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers.
Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement
For dual-channel speech enhancement, it is a promising idea to design an end-to-end model based on the traditional array signal processing guideline and the manifold space of multi-channel signals.
Guided Training: A Simple Method for Single-channel Speaker Separation
Another way is to use an anchor speech, a short speech of the target speaker, to model the speaker identity.
Speakerfilter-Pro: an improved target speaker extractor combines the time domain and frequency domain
This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on our previous Speakerfilter model.
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks
We show that our model is able to improve the performance of model, compared with existing convolutional recurrent networks.
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting
To improve the robustness, a speech enhancement front-end is involved.
Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
In this paper, we use the optimal ratio mask as the training target of the deep neural network (DNN) for speech separation.
